In view of the general problems of not fully utilizing historical information and slow parameter optimization process in the research of clustering algorithms， an adaptive classification algorithm based on data field was proposed in combination with edge intelligent computing， which can be deployed on Edge Computing （EC） nodes to provide local intelligent classification service. By introducing supervision information to modify the structure of the traditional data field clustering model， the proposed algorithm enabled the traditional data field to be applied to classification problems， extending the applicable fields of data field theory. Based on the idea of the data field， the proposed algorithm transformed the domain value space of the data into the data potential field space， and divided the data into several unlabeled cluster results according to the spatial potential value. After comparing the cluster results with the historical supervision information for cloud similarity， the cluster results were attributed to the most similar category. Besides， a parameter search strategy based on sliding step length was proposed to speeded up the parameter optimization of the proposed algorithm. Based on this algorithm， a distributed data processing scheme was proposed. Through the cooperation of cloud center and edge devices， classification tasks were cut and distributed to different levels of nodes to achieve modularity and low coupling. Simulation results show that the precision and recall of the proposed algorithm maintained above 96%， and the Hamming loss was less than 0.022. Experimental results show that the proposed algorithm can accurately classify and accelerate the speed of parameter optimization， and outperforms than Logistic Regression （LR） algorithm and Random Forest （RF） algorithm in overall performance.